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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

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Accuracy Evaluation of Soft Classifiers using Interval Type-2 Fuzzy Sets Framework

Accuracy Evaluation of Soft Classifiers using Interval Type-...

引用

IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Binaghi, Elisabetta Vergani, Alberto A. Pedoia, Valentina Univ Insubria Dept Theoret & Appl Sci Varese Italy UCSF Sch Med Dept Radiol San Francisco CA USA

ISBN: (纸本)9781509060344

This paper proposes a new accuracy evaluation method within a behavioral comparison strategy which uses interval type-2 fuzzy sets and derived operations to model reference data and define soft accuracy indexes. The method addresses the case in which grades of membership, collected by surveying experts, will often be different for the same reference pattern, because the experts will not necessarily be in agreement. The approach is illustrated using simple examples and an application in the domain of biomedical image segmentation.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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A Tensor-based Technique for Structure-aware image Inpainting 6

A Tensor-based Technique for Structure-aware Image Inpaintin...

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6th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Akl, Adib Yaacoub, Charles Holy Spirit Univ Kaslik USEK Fac Engn Jounieh Lebanon

ISBN: (纸本)9789897582226

image inpainting is an active area of study in computer graphics, computer vision and image processing. Different image inpainting algorithms have been recently proposed. Most of them have shown their efficiency with different image types. However, failure cases still exist, especially when dealing with local image variations. This paper presents an image inpainting approach based on structure layer modeling, where this latter is represented by the second-moment matrix, also known as the structure tensor. The structure layer of the image is first inpainted using the non-parametric synthesis algorithm of Wei and Levoy, then the inpainted field of second-moment matrices is used to constrain the inpainting of the image itself. Results show that using the structural information, relevant local patterns can be better inpainted comparing to the standard intensity-based approach.

关键词： image Inpainting Orientation Second-moment Matrix Exemplar

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Generalizing an Interval-Valued image Magnification Algorithm Using Homogeneity Measures and Interval Fusion Functions 17

Generalizing an Interval-Valued Image Magnification Algorith...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Paternain, Daniel Jurio, Aranzazu Pagola, Miguel Barrenechea, Edurne Bustince, Humberto Univ Publ Navarra Dept Automat & Comp Pamplona 31006 Spain Univ Publ Navarra Inst Smart Cities Pamplona 31006 Spain

ISBN: (纸本)9781509049172

In this work we study and generalize an image magnification algorithm based on the use of interval-valued fuzzy sets. The first proposed generalization incorporates an homogeneity measure that allows to model the length of the intervals generated by the algorithm. The second one makes use of several homogeneity measures and, by means of a fusion function, it combines the intervals generated by each individual homogeneity measure. The results show that our generalization outperforms the original algorithm when an appropriate homogeneity measure is used. Moreover, experiments have demonstrated that the second generalization, based on interval fusion functions, avoids low quality results due to bad homogeneity measures.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Principal Component Analysis Approach in Selecting Type-1 and Type-2 Fuzzy Membership Functions for High-dimensional Data 17

Principal Component Analysis Approach in Selecting Type-1 an...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Raj, Desh Gupta, Aditya Tanna, Kenil Garg, Bhuvnesh Rhee, Frank Chung-Hoon Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati 781039 India Indian Inst Technol Guwahati Dept Math Gauhati 781039 India Hanyang Univ Dept Elect & Commun Engn Ansan 15588 South Korea

ISBN: (纸本)9781509049172

With increased interest in learning from data, algorithms that manipulate datasets containing hundreds of features have become popular in various fields such as medicine, image processing, geolocation, biochemistry, and computational linguistics. Since a number of these applications exploit the power of fuzzy sets in representing uncertainties, it may be considered essential to describe a method for selecting the most suitable fuzzy membership function to represent a high-dimensional dataset. In this paper, we propose such a method, which is based on dimensionality reduction using the Principal Component Analysis (PCA) technique, followed by the Wilcoxon Minimal Bin Size algorithm, which has earlier been evaluated on multidimensional datasets up to 8 dimensions. We further demonstrate our proposed method using two real datasets consisting of 281 and 500 features, respectively.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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ACE: An Efficient Asynchronous Corner Tracker for Event Cameras

ACE: An Efficient Asynchronous Corner Tracker for Event Came...

引用

International conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Ignacio Alzugaray Margarita Chli Vision for Robotics Lab ETH Zürich

The emergence of bio-inspired event cameras has opened up new exciting possibilities in high-frequency tracking, overcoming some of the limitations of traditional frame-based vision (e.g. motion blur during high-speed motions or saturation in scenes with high dynamic range). As a result, research has been focusing on the processing of their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream into frame-like representations, we are yet to harness the true power of these cameras. In this paper, we propose the ACE tracker: a purely asynchronous framework to track corner-event features. Evaluation on benchmarking datasets reveals significant improvements in accuracy and computational efficiency in comparison to state-of-the-art event-based trackers. ACE achieves robust performance even in challenging scenarios, where traditional frame-based vision algorithms fail.

关键词： Cameras Tracking image edge detection Simultaneous localization and mapping Feature extraction Robot vision systems Visualization

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Everything is image: CNN-based Short-term Electrical Load Forecasting for Smart Grid 14

Everything is Image: CNN-based Short-term Electrical Load Fo...

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14th International Symposium on Pervasive systems, algorithms and Networks (I-SPAN) / 11th International conference on Frontier of Computer Science and Technology (FCST) / 3rd International Symposium of Creative Computing (ISCC)

作者： Li, Liangzhi Ota, Kaoru Dong, Mianxiong Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido Japan

ISBN: (纸本)9781538608401

Electrical load forecasting is of great significance to guarantee the system stability under large disturbances, and optimize the distribution of energy resources in the smart grid. Traditional prediction models, which are mainly based on time series analyzing, have been unable to fully meet the actual needs of the power system, due to their non-negligible prediction errors. To improve the forecasting precision, we successfully transform the numerical prediction problem into an image processing task, and, based on that, utilize the state-of-the-art deep learning methods, which have been widely used in computer image area, to perform the electrical load forecasting. A novel deep learning based short-term forecasting (DLSF) method is proposed in the paper. Our method can perform accurate clustering on the input data using a deep Convolutional Neural Network (CNN) model. And ultimately, another neural network with three hidden-layers is used to predict the electric load, considering various external influencing factors, e.g. temperature, humidity, wind speed, etc. Experimental results demonstrate that the proposed DLSF method performs well in both accuracy and efficiency.

关键词： Load forecasting Neural networks Forecasting Support vector machines Machine learning Load modeling Training

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A Survey on Performance of Various image Compression Techniques

A Survey on Performance of Various Image Compression Techniq...

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International conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)

作者： H R Gireesh L Rangaiah VTU Belagavi India RRCE Bangalore India

ISBN: (数字)9781538651308

ISBN: (纸本)9781538651315

image compression enables quite exciting solutions in many fields, such as image analysis, bio-medical image processing, wireless systems and seems to be a key application in today's digital and smart world. image compression seems to be a powerful tool in case of transmission and storage of large data images in various applications such as big data, medical etc. However due to exclusive and quality soft tissue contrast, Dynamic Magnetic Resonance Imaging (MRI) has been a field of attraction with increasing attention in recent decades. Moreover, MRI is considered as one of the most effective and strongest diagnosis system making the extensive usage of magnetic and radio waves in order to diagnose the human organs. This diagnosis is capable of generating 3D images with detailed anatomical features without any X-ray radiations. The prime purpose of this survey is to provide a comprehensive report of different image compression schemes in order to design an efficient compression scheme for dynamic MRI images. In this paper, author has surveyed different image compression schemes which are either sole implementations or hybrid of two or more algorithms. The author has also presented a comparative analysis for the surveyed compression schemes. This survey paper finally makes inroads for further researches in the domain of image compression schemes for dynamic MRI images.

关键词： image coding Magnetic resonance imaging Discrete cosine transforms Biomedical imaging Entropy Discrete wavelet transforms

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Use of OWA Operators for Feature Aggregation in image Classification

Use of OWA Operators for Feature Aggregation in Image Classi...

引用

IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Pagola, Miguel Forcen, Juan I. Barrenechea, Edurne Lopez-Molina, Carlos Bustince, Humberto Univ Publ Navarra Inst Smart Cities Pamplona 31006 Spain

ISBN: (纸本)9781509060344

Feature aggregation is a crucial step in many methods of image classification, like the Bag-of-Words (BoW) model or the Convolutional Neural Networks (CNN). In this aggregation step, usually known as spatial pooling, the descriptors of neighbouring elements within a region of the image are combined into a local or a global feature vector. The combined vector must contain relevant information, while removing irrelevant and confusing details. Maximum and average are the most common aggregation functions used in the pooling step. To improve the aggregation of relevant information without degrading their discriminative power for classification in this work we propose the use of Ordered Weighted operators. We provide an extensive evaluation that shows that the final result of the classification using OWA aggregation is always better than average pooling and better than maximum pooling when dealing with small dictionary sizes.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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image denoising and deblurring using multispectral data 23

Image denoising and deblurring using multispectral data

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23rd SPIE conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery

作者： Semenishchev, E. A. Voronin, V. V. Marchuk, V. I. Don State Tech Univ Dept Radioelect Syst Shevchenko 147 Shakhty 346500 Russia

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

Currently decision-making systems get widespread. These systems are based on the analysis video sequences and also additional data. They are volume, change size, the behavior of one or a group of objects, temperature gradient, the presence of local areas with strong differences, and others. Security and control system are main areas of application. A noise on the images strongly influences the subsequent processing and decision making. This paper considers the problem of primary signal processing for solving the tasks of image denoising and deblurring of multispectral data. The additional information from multispectral channels can improve the efficiency of object classification. In this paper we use method of combining information about the objects obtained by the cameras in different frequency bands. We apply method based on simultaneous minimization L2 and the first order square difference sequence of estimates to denoising and restoring the blur on the edges. In case of loss of the information will be applied an approach based on the interpolation of data taken from the analysis of objects located in other areas and information obtained from multispectral camera. The effectiveness of the proposed approach is shown in a set of test images.

关键词： saliency map image edge detectors density

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Novel segmentation for twig problem by adding prior nodes in Random-Walk algorithm 2

Novel segmentation for twig problem by adding prior nodes in...

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2nd International conference on Communication and Electronics systems, ICCES 2017

作者： Chougule, Keshav J. Subhedar, Mansi S. Department of Electronics and Telecommunication Pillai HOC College of Engineering and Technology Rasayani Raigad. Maharashtra410206 India

ISBN: (纸本)9781509050130

In object identification image Segmentation is the first step in digital image processing. It can be used to compress different segments or areas of image. A novel sub-Markov Random Walk (subRW) algorithm with label prior is proposed for seeded image segmentation. It is traditional random walker on a graph with added auxiliary nodes. The uniqueness will be nothing but adding or changing the auxiliary nodes in segmentation algorithm. We face segmentation problem in existing system if the image having very thin and elongated parts. we design a new sub RW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts (i.e Twig Problem). The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous Well Known RW algorithms for segmentation. © 2017 IEEE.

关键词： image segmentation

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